AI agent development education
A bilingual open-source AI Agent development textbook covering LLM agents, RAG, tool use, memory, multi-agent systems, and agentic RL.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose it if you prefer an Apache-licensed Chinese book with compiled PDF and per-chapter code over the seed's bilingual markdown and SVG diagrams.
《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码
Choose it if you want a Chinese crash course with interview questions and enterprise deployment/fine-tuning coverage.
🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化
Choose it if you want a Chinese from-scratch agent-building tutorial with a different explanation style.
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
Choose it if you prefer runnable Jupyter notebooks for 50+ agent techniques over a structured textbook.
50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems.
Choose it if you want code-first production deployment tutorials rather than a from-scratch textbook.
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Choose it if you want a smaller collection of runnable agent project tutorials focused on multi-agent, memory, and planning.
Multi-agent systems, memory, planning, reasoning loops
Choose it if you want a JavaScript-based skeleton course on designing and operating production agents.
Use agent to learn agent - A skeleton course on how to design, build, and operate production AI agents
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose it if you want a broader AI engineering curriculum rather than a dedicated agent-building textbook.
Learn it. Build it. Ship it for others.
Choose it if you want a broader AI application development and interview guide rather than a focused agent textbook.
AI 应用开发、AI 编程实战与面试指南，涵盖 LLM、Agent、RAG、MCP、Claude Code、Codex 等核心技术与工程实践。
Choose it if you want an AI system design and evals guide rather than a hands-on agent-building tutorial.
AI system design guide for engineers building production AI systems and evals.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose it if you need a narrow, notebook-based deep dive into agent memory techniques only.
Agent memory for LLMs: 30 runnable Jupyter notebooks covering conversation buffers, vector stores, knowledge graphs, episodic and semantic memory, MemGPT, Mem0, Letta, Zep, Graphiti, LoCoMo benchmarks, and production patterns.
Choose it if you only need a focused collection of RAG technique notebooks rather than a full agent curriculum.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Choose it if you want a deep dive into Claude Code's agent harness architecture, not a general agent-building textbook.
《御舆：解码 Agent Harness》42万字拆解 AI Agent 的Harness骨架与神经 —— Claude Code 架构深度剖析，15 章从对话循环到构建你自己的 Agent Harness。在线阅读网站：
These projects were analysed and named agent_learning among their alternatives. The relationship is not symmetric — how agent_learning rates them is a separate judgement, made when agent_learning is analysed in its own right.
calls agent_learning “If you prefer an organized single-page curriculum with explicit sections on multi-agent and RAG.”
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
calls agent_learning “Choose it if you prefer an HTML site to read; the seed is a Python-repo course that also ships an interview bank.”
🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化
calls agent_learning “Choose it for a similarly structured Chinese agent curriculum that adds agentic-RL coverage the seed skips.”
AI 应用开发、AI 编程实战与面试指南，涵盖 LLM、Agent、RAG、MCP、Claude Code、Codex 等核心技术与工程实践。
calls agent_learning “Choose when you want an original step-by-step agent tutorial instead of an aggregated job-prep guide.”
https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成